{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Trading Rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sysdata.csv.csvfuturesdata import csvFuturesData\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work up a minimum example of a trend following system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get some data\n",
    "\n",
    "We can get data from various places; however for now we're going to use\n",
    "prepackaged 'legacy' data stored in csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FuturesData object with 38 instruments"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = csvFuturesData()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get stuff out of data with methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CORN', 'LEANHOG', 'LIVECOW', 'SOYBEAN', 'WHEAT', 'KR10', 'KR3', 'BOBL', 'BTP', 'BUND', 'OAT', 'SHATZ', 'US10', 'US2', 'US20', 'US5', 'V2X', 'VIX', 'KOSPI', 'AEX', 'CAC', 'SMI', 'NASDAQ', 'SP500', 'AUD', 'EUR', 'GBP', 'JPY', 'MXP', 'NZD', 'COPPER', 'GOLD', 'PALLAD', 'PLAT', 'CRUDE_W', 'GAS_US', 'EDOLLAR', 'EUROSTX']\n",
      "2016-11-07    98.5600\n",
      "2016-11-08    98.5100\n",
      "2016-11-09    98.2950\n",
      "2016-11-10    98.1600\n",
      "2016-11-11    98.1175\n",
      "Name: price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data.get_instrument_list())\n",
    "print(data.get_raw_price(\"EDOLLAR\").tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data can also behave in a dict like manner (though it's not a dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1997-09-10    1047.6725\n",
       "1997-09-11    1040.6725\n",
       "1997-09-12    1056.6725\n",
       "1997-09-15    1054.6725\n",
       "1997-09-16    1078.6725\n",
       "1997-09-17    1078.6725\n",
       "1997-09-18    1080.6725\n",
       "1997-09-19    1083.6725\n",
       "1997-09-22    1088.6725\n",
       "1997-09-23    1084.6725\n",
       "1997-09-24    1077.6725\n",
       "1997-09-25    1069.6725\n",
       "1997-09-26    1075.6725\n",
       "1997-09-29    1084.6725\n",
       "1997-09-30    1076.6725\n",
       "1997-10-01    1086.6725\n",
       "1997-10-02    1091.6725\n",
       "1997-10-03    1097.6725\n",
       "1997-10-06    1104.6725\n",
       "1997-10-07    1112.6725\n",
       "1997-10-08    1105.6725\n",
       "1997-10-09    1100.6725\n",
       "1997-10-10    1099.6725\n",
       "1997-10-13    1098.6725\n",
       "1997-10-14    1099.6725\n",
       "1997-10-15    1096.6725\n",
       "1997-10-16    1082.6725\n",
       "1997-10-17    1071.6725\n",
       "1997-10-20    1085.6725\n",
       "1997-10-21    1101.6725\n",
       "                ...    \n",
       "2016-10-03    2153.5000\n",
       "2016-10-04    2144.7500\n",
       "2016-10-05    2153.5000\n",
       "2016-10-06    2156.5000\n",
       "2016-10-07    2146.5000\n",
       "2016-10-10    2159.2500\n",
       "2016-10-11    2134.5000\n",
       "2016-10-12    2131.5000\n",
       "2016-10-13    2126.5000\n",
       "2016-10-14    2127.0000\n",
       "2016-10-17    2123.8750\n",
       "2016-10-18    2132.2500\n",
       "2016-10-19    2138.5000\n",
       "2016-10-20    2137.0000\n",
       "2016-10-21    2134.7500\n",
       "2016-10-24    2144.5000\n",
       "2016-10-25    2138.5000\n",
       "2016-10-26    2133.7500\n",
       "2016-10-27    2124.0000\n",
       "2016-10-28    2123.7500\n",
       "2016-10-31    2123.5000\n",
       "2016-11-01    2102.0000\n",
       "2016-11-02    2090.2500\n",
       "2016-11-03    2083.2500\n",
       "2016-11-04    2081.5000\n",
       "2016-11-07    2130.0000\n",
       "2016-11-08    2134.7500\n",
       "2016-11-09    2160.5000\n",
       "2016-11-10    2166.7500\n",
       "2016-11-11    2150.8750\n",
       "Name: price, Length: 5003, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SP500']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CORN',\n",
       " 'LEANHOG',\n",
       " 'LIVECOW',\n",
       " 'SOYBEAN',\n",
       " 'WHEAT',\n",
       " 'KR10',\n",
       " 'KR3',\n",
       " 'BOBL',\n",
       " 'BTP',\n",
       " 'BUND',\n",
       " 'OAT',\n",
       " 'SHATZ',\n",
       " 'US10',\n",
       " 'US2',\n",
       " 'US20',\n",
       " 'US5',\n",
       " 'V2X',\n",
       " 'VIX',\n",
       " 'KOSPI',\n",
       " 'AEX',\n",
       " 'CAC',\n",
       " 'SMI',\n",
       " 'NASDAQ',\n",
       " 'SP500',\n",
       " 'AUD',\n",
       " 'EUR',\n",
       " 'GBP',\n",
       " 'JPY',\n",
       " 'MXP',\n",
       " 'NZD',\n",
       " 'COPPER',\n",
       " 'GOLD',\n",
       " 'PALLAD',\n",
       " 'PLAT',\n",
       " 'CRUDE_W',\n",
       " 'GAS_US',\n",
       " 'EDOLLAR',\n",
       " 'EUROSTX']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... however this will only access prices\n",
    "(note these prices have already been backadjusted for rolls)\n",
    "\n",
    "We have extra futures data here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRICE</th>\n",
       "      <th>CARRY</th>\n",
       "      <th>CARRY_CONTRACT</th>\n",
       "      <th>PRICE_CONTRACT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-04</th>\n",
       "      <td>98.6250</td>\n",
       "      <td>98.675</td>\n",
       "      <td>201906</td>\n",
       "      <td>201909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>98.5600</td>\n",
       "      <td>98.610</td>\n",
       "      <td>201906</td>\n",
       "      <td>201909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-08</th>\n",
       "      <td>98.5100</td>\n",
       "      <td>98.565</td>\n",
       "      <td>201906</td>\n",
       "      <td>201909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-09</th>\n",
       "      <td>98.2950</td>\n",
       "      <td>98.365</td>\n",
       "      <td>201906</td>\n",
       "      <td>201909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10</th>\n",
       "      <td>98.1600</td>\n",
       "      <td>98.230</td>\n",
       "      <td>201906</td>\n",
       "      <td>201909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-11</th>\n",
       "      <td>98.1175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201906</td>\n",
       "      <td>201909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PRICE   CARRY CARRY_CONTRACT PRICE_CONTRACT\n",
       "2016-11-04  98.6250  98.675         201906         201909\n",
       "2016-11-07  98.5600  98.610         201906         201909\n",
       "2016-11-08  98.5100  98.565         201906         201909\n",
       "2016-11-09  98.2950  98.365         201906         201909\n",
       "2016-11-10  98.1600  98.230         201906         201909\n",
       "2016-11-11  98.1175     NaN         201906         201909"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.get_instrument_raw_carry_data(\"EDOLLAR\").tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Technical note: csvFuturesData inherits from FuturesData which itself inherits\n",
    "from Data\n",
    "The chain is 'data specific' <- 'asset class specific' <- 'generic'\n",
    "\n",
    "So there are also\n",
    "\n",
    "In principal there could be an equities data\n",
    "\n",
    "Let's create a simple trading rule\n",
    "\n",
    "No capping or scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from syscore.algos import robust_vol_calc\n",
    "\n",
    "\n",
    "def calc_ewmac_forecast(price, Lfast, Lslow=None):\n",
    "    \"\"\"\n",
    "    Calculate the ewmac trading fule forecast, given a price and EWMA speeds\n",
    "    Lfast, Lslow and vol_lookback\n",
    "\n",
    "    \"\"\"\n",
    "    # price: This is the stitched price series\n",
    "    # We can't use the price of the contract we're trading, or the volatility\n",
    "    # will be jumpy\n",
    "    # And we'll miss out on the rolldown. See\n",
    "    # http://qoppac.blogspot.co.uk/2015/05/systems-building-futures-rolling.html\n",
    "\n",
    "    price = price.resample(\"1B\").last()\n",
    "\n",
    "    if Lslow is None:\n",
    "        Lslow = 4 * Lfast\n",
    "\n",
    "    # We don't need to calculate the decay parameter, just use the span\n",
    "    # directly\n",
    "    fast_ewma = price.ewm(span=Lfast).mean()\n",
    "    slow_ewma = price.ewm(span=Lslow).mean()\n",
    "    raw_ewmac = fast_ewma - slow_ewma\n",
    "    vol = robust_vol_calc(price.diff())\n",
    "    return raw_ewmac / vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Try it out\n",
    "\n",
    "(this isn't properly scaled at this stage of course)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-11-07    0.100716\n",
       "2016-11-08   -0.011247\n",
       "2016-11-09   -0.237658\n",
       "2016-11-10   -0.513974\n",
       "2016-11-11   -0.835147\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instrument_code = 'EDOLLAR'\n",
    "price = data.daily_prices(instrument_code)\n",
    "ewmac = calc_ewmac_forecast(price, 32, 128)\n",
    "ewmac.columns = ['forecast']\n",
    "ewmac.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111b35f98>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuaAwM7OOXFCYmVlHLijMzKyj/wF1qARbzQyBEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111cf7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ewmac.plot();\n",
    "plt.title('Forecast')\n",
    "plt.ylabel('Position')\n",
    "plt.xlabel('Time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Did we make money?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accountCurve?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZUnBGGOM1/8HRGK6caTF4b4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111df3e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from syscore.accounting import accountCurve\n",
    "account = accountCurve(price, forecast=ewmac)\n",
    "account.curve().plot();\n",
    "plt.title('Profit and Loss')\n",
    "plt.ylabel('PnL')\n",
    "plt.xlabel('Time');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('min', '-7.911'),\n",
       "  ('max', '5.22'),\n",
       "  ('median', '0'),\n",
       "  ('mean', '0.01602'),\n",
       "  ('std', '0.515'),\n",
       "  ('skew', '-0.5705'),\n",
       "  ('ann_mean', '4.102'),\n",
       "  ('ann_std', '8.24'),\n",
       "  ('sharpe', '0.4978'),\n",
       "  ('sortino', '0.5583'),\n",
       "  ('avg_drawdown', '-11.93'),\n",
       "  ('time_in_drawdown', '0.9735'),\n",
       "  ('calmar', '0.1237'),\n",
       "  ('avg_return_to_drawdown', '0.3439'),\n",
       "  ('avg_loss', '-0.3289'),\n",
       "  ('avg_gain', '0.3399'),\n",
       "  ('gaintolossratio', '1.033'),\n",
       "  ('profitfactor', '1.118'),\n",
       "  ('hitrate', '0.5195'),\n",
       "  ('t_stat', '2.891'),\n",
       "  ('p_value', '0.003851')],\n",
       " ('You can also plot / print:',\n",
       "  ['rolling_ann_std', 'drawdown', 'curve', 'percent', 'cumulative'])]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account.percent().stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}